ML models are only as good as the datasets they're trained on, and that means that most improvement to model performance comes from improvement to the quality and diversity of their datasets. Our tooling makes it easy for ML teams to find anomalies + failure patterns in their datasets and fix these problems by editing / adding the right data. So the next time you retrain your model, it just gets better.
Peter was an early employee (#18) at Cruise, where he built a large part of a self driving car from scratch and led the computer vision team and was the tech lead for the overall perception team. Before that, Peter did research on deep learning for object detection at UC Berkeley. Before that, he interned at Pinterest and Khan Academy, doing a mix of ML and web work. Now he's founding a company building deep learning pipelines that can improve themselves!
Quinn is an engineer/manager who picked a *fantastic* time to co-found a company making deep learning pipelines that improve themselves. Before that he was at Ouster (leading data engineering / data viz), Cruise Automation (leading ML data engineering + labeling), and Graphistry (1st engineering hire, so a bit of everything). Working on self-driving cars has given him an irrational hatred for trees and shrubbery.